Biomarkers and imaging

About

• Use medical imaging biomarkers to improve our understanding and management of multimorbidity and organ interactions in human disease.

• Offer infrastructure, technical solutions and support for integrating medical imaging biomarkers as primary or secondary endpoints in clinical or preclinical research.

Why imaging biomarkers?

The study of multi-organ diseases requires – ideally – multi-organ data. While individual organs can be assessed in animal models through invasive measurement, the utility of tissue sampling to study human disease is extremely limiting. This is problematic, because animal models often poorly replicate human diseases that affect multiple organs and may present at the same time, such as diabetes, hypertension or obesity. 

As a result, it is common that findings on disease mechanisms, or on safety and efficacy of novel treatments, do not translate from animal models to humans. In clinical practice, physicians are often blind to the effect of their treatment on the target organ, and instead need to rely on less specific markers in biofluids to plan and evaluate management strategies. Overcoming these challenges requires more sensitive markers that can assess the impact of multi-organ disease in-situ, non-invasively, and in humans.

Medical imaging biomarkers promise to fill this gap in the diagnostic spectrum. While imaging traditionally has focused on visual assessment of anatomy, regulators such as FDA and EMA currently accept imaging as a source of biomarkers alongside more traditional sources such as biofluids, tissues or physiological measurement. Imaging biomarkers – especially those derived from Magnetic Resonance Imaging (MRI) - are sensitive to features at different scales, such as microstructure, haemodynamics, metabolism, perfusion, function, stiffness and elasticity, inflammation, fibrosis, lipid content, sodium concentration or tissue composition. Because data are taken locally in the tissue of interest, this can be thought of as a “virtual biopsy” – albeit one that is able to assess multiple organs at the same time, and without disturbing the natural state of tissue.

What do we offer?

The medical imaging group in Sheffield University are a multidisciplinary team who develop novel imaging and healthcare engineering technologies and translate them for healthcare benefit. Our methods are underpinned and enhanced by computational, physical and physiological modelling, and integrated within INSIGNEO. Our core expertise ranges from applications in brain, lungs, heart, and abdominal organs providing an excellent environment for the study of multi-organ effects.

We offer state-of-the art infrastructure for human and preclinical medical imaging and have labs for developing tailored hardware including for highly innovative techniques such as hyperpolarised MRI. We have tried-and-tested solutions for data management, quality assurance and image processing for imaging biomarkers in large scale studies. Example applications are the ongoing UK-wide C-MORE study on long COVID or the European study iBEAt on diabetes and kidney disease. 

See here for more detail on research themes, facility and people.

Who can benefit?

Researchers in academia, healthcare or industry – if you are planning clinical or preclinical studies on multi-morbidity that can be enriched by including imaging biomarkers, we can help you:

• identify suitable imaging biomarkers;
• propose acquisition protocols;
• provide cost estimates;
• implement them either on your local scanners, or 
• run them in the image centres in Sheffield;
• offer image processing solutions or services to extract the biomarkers. 

We are open to different models of collaboration – we ran studies in house and process your data centrally, or, if you have the capacity, we can support setup of your scanners and provide you with software and training so you can process your own images. Mixed models, such as local data acquisition with central analysis, are also possible.

I am interested – what next?

Get in touch with the imaging group in Sheffield University as early as possible in the development of your ideas for explorative discussions. While imaging biomarkers can sometimes be added at later stages or even extracted retrospectively, their utility and practicality will be greatly enhanced by tailoring endpoints and experimental design prospectively to the particular research question.

More detail and contact info: https://www.sheffield.ac.uk/medicine/research/research-themes/medical-imaging/medical-imaging-facilities/planning-new-mri-study

Methodologies & capabilities

• MRI infrastructure: 3T human whole body PET/MRI, 2 x 1.5T multi-nuclear whole-body human MRI, preclinical 7T and 9.4T, regulatory approved hyperpolarisation lab, rf-coil and MR Engineering lab.
• Data management: well-supported setup for securing and managing patient data in a multi-centre setting on an XNAT platform.
• Image processing: customisable multi-organ software solutions for image processing (motion correction, image alignment, segmentation) and modelling and visualisation of a wide range of MRI contrasts (such as diffusion- and diffusion tensor MRI, perfusion ASL and DCE, lung function and ventilation, X-nuclei, volumetry and morphometry, relaxation times T1, T2 and T2*).

Staff and PhD students

https://www.sheffield.ac.uk/medicine/research/research-themes/medical-imaging/medical-imaging-our-people

Publications

• Quantitative Magnetic Resonance Imaging, Editors: Nicole Seiberlich, Vikas Gulani, Adrienne Campbell, Steven Sourbron, Mariya Ivanova Doneva, Fernando Calamante, Houchun Harry Hu, Elsevier, p1-1006 (2020)
• Alabed S, et al (2022) Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction. Radiology, 212929
• Grist JT, et al (2022) Lung Abnormalities Depicted with Hyperpolarized Xenon MRI in Patients with Long COVID. Radiology, 220069
• Alabed S, et al (2022) Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension. European Heart Journal - Digital Health
• Evans RA, et al (2022) Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study. The Lancet Respiratory Medicine.
• Elsharif, M. Hepatectomy risk assessment with functional magnetic resonance imaging (HEPARIM). BMC Cancer (2021) 21(1): 1-10
• Sassani M, Alix J, Mcdermott C, Baster K, Hoggard N, Wild J, Mortiboys H, Shaw P, Wilkinson I & Jenkins T (2021) Brain energy metabolism in ALS: A phosphorus-31 magnetic resonance spectroscopy study. Journal of the Neurological Sciences, 429, 119412-119412
• Evans RA, et al (2021) Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. The Lancet Respiratory Medicine
• Mamalakis M, et al (2021) MA-SOCRATIS: An automatic pipeline for robust segmentation of the left ventricle and scar. Computerized Medical Imaging and Graphics, 93, 101982-101982
• Sassani M, et al (2021) Brain energy metabolism in ALS: A phosphorus-31 magnetic resonance spectroscopy study. Journal of the Neurological Sciences, 429, 119412
• Scotcher et al. (2021) Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats. Molecular pharmaceutics 18(8) 2997-3009.
• Croall ID, Sanders DS, Hadjivassiliou M & Hoggard N (2020) Cognitive deficit and white matter changes in persons with celiac disease: a population-based study. Gastroenterology.

Case Studies

The iBEAt study on diabetic kidney disease including multi-organ assessment in kidney, pancreas and liver. This is a Europe wide imaging biomarkers study in 550 patients led by Sheffield with recruiting centres in UK, Italy, Finland and France, and central biobanking in Sweden. Study protocol:

• Gooding KM et al. Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol. BMC Nephrology (2020) 21(1): 1-11.

The SUGAR trial on the effect of SGLT2-inhibitors in heart failure with reduced ejection fraction (led by Glasgow). We provided image acquisition and analysis solutions along with training for cardiac and renal perfusion endpoints. Results reported in:

• Lee MMY, et al (2022) Effect of Empagliflozin on Kidney Biochemical and Imaging Outcomes in Patients With Type 2 Diabetes, or Prediabetes, and Heart Failure with Reduced Ejection Fraction (SUGAR-DM-HF). Circulation, 146(4), 364-367
• Lee MMY, et al (2021) Effect of empagliflozin on left ventricular volumes in patients with type 2 diabetes, or prediabetes, and heart failure with reduced ejection fraction (SUGAR-DM-HF). Circulation, 143(6), 516-525

The C-MORE study on multi-organ effects of COVID19 (led by Oxford). Pulmonary imaging endpoints provided by Sheffield. Study registration: https://clinicaltrials.gov/ct2/show/NCT04510025